Stories That Heal: Understanding the Effects of Creating and Viewing Digital Stories with Pediatric Oncology Patients, Families, and Healthcare Teams
Bibliographic record
Abstract
In this paper, we discuss a qualitative research study that is focused on understanding the meaning, experience, and therapeutic value of digital stories with pediatric oncology patients and families, and with the pediatric oncology health care team. With children, we are interested in understanding the process of creating a digital story; with the families and the health care team, we are interested in understanding what happens to them when they view the digital stories of their children and the patients with whom they work. Ultimately, we wish to determine if, and understand how, digital stories might be effective therapeutic tools to use with children and their families, thus helping to mitigate suffering, and effective tools for the pediatric oncology health care team as a way of providing insight and understanding into patients' and families' unique experiences with childhood cancer. This research will be guided by philosophical hermeneutics, which is defined as the art and practice of interpretation, and is a particularly sophisticated approach to bring to situations where understanding is sought. The study has been approved and funded and we offer the background and design for it.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".